Other Important Topics in Data Mining that we didn’t or very little discuss in this class

1. Other Important Topics in Data Mining that we didn’t or very little discuss in this class • Mining Data Steams / Incremental Data Mining / Mining sensor data (e.g. modify a decision tree assuming that new examples arrive continuously, and old examples are discarded) • Text Mining • Mining the Web/Mining Graphs and other complex structures • Mining spatial-temporal data, particularly environmental, cell-phone, and traffic data • Contrast mining (e.g. how do two groups of people differ) • Data Mining and Privacy • Mining Social Networks (kind of hot these days) • Statistical Techniques (Principal component analysis, multi-dimensional scaling, feature selection, statistical testing, Bayesian classifier,...)typically taught in a Machine Learning class. • Preprocessing probably deserves more coverage • High Performance Data Mining Parallel Programming Course

2. New Challenges for the Field of Data Mining • Develop a unifying theory for data mining (e.g. explaining how and when over-fitting occurs) • Mining data streams / mining sensor networks / mining sequential data • High performance data mining platforms / combining parallel computing and data mining (http://en.wikipedia.org/wiki/Hadoop) • Spatial data mining / temporal data mining / spatial temporal • Mining graphs and other complex types of data • More research on the interestingness of knowledge • Distributed data mining (cannot pass the complete data set; distributed decision making, e.g. in sensor networks) • Data mining for genomic and earth science problems • What is the data mining process --- kind of software engineering for data mining; development of data mining methodologies… • Data Mining without violating privacy and security

3. Complementary Knowledge For Getting Jobs in Data Mining Search Techniques Evolutionary Computing Information Retrieval Databases Software Design Pattern Recognition Data Visualization Data Mining High Performance Computing AI Machine Learning Image Processing Data Structures & Algorithms GIS Experimental Evaluation Optimization Statistics Software Engineering

4. 2008 Student Textbook Evaluation • Overall positive evaluation but • Some felt that algorithms were not explained in sufficient detail, particularly examples are missing • A few felt the material should be better indexed • Some felt it lack highlighting of key points • Some felt it is at an intermediate level, and does not give sufficient depth if the textbook is your only source of knowledge; it also introduces topics more intuitively and not formally, as some more advanced textbook do. • 2 students felt that the textbook does not introduce topics very clearly, and that it is not comprehensive.